Machine Learning
Neural Computation
| Hi-index | 0.02 |
Motor unit action potentials (MUAPs) recorded during routine electromyography (EMG) examination provide important information for the assessment of neuromuscular disorders, and the neural network based MUAPs classification system has been used to enhance the diagnosis accuracy. However, the conventional neural networks methods of MUAP diagnosis are mainly based on single feature set model, and the diagnosis accuracy of which is not always satisfactory. In order to utilize multiple feature sets to improve diagnosis accuracy, a hybrid decision support system based on fusion multiple neural networks outputs is presented. Back-propagation (BP) neural network is used as single diagnosis model in every feature set, i.e. i) time domain morphological measures, ii) frequency parameters, and iii) time-frequency domain wavelet transform feature set. Then these outputs are combined by fuzzy integral. More excellent diagnosis yield indicates the potential of the proposed multiple neural networks strategies for neuromuscular disorders diagnosis.